Methods

Data was processed using ebi-metagenomics/assembly-analysis-pipeline v6.0.0-beta (doi: 10.1093/nar/gkac1080), this pipeline is part of EMBL-EBI's metagenomics platform.

The pipeline was executed with Nextflow v24.10.3 (Di Tommaso et al., 2017) with the following command:

nextflow run ebi-metagenomics/assembly-analysis-pipeline -r dev -latest -config /hps/software/users/rdf/metagenomics/service-team/configs/pipelines-nextflow-configs/configs/assembly-analysis-pipeline/codon.config -profile codon -resume --input /hps/nobackup/rdf/metagenomics/service-team/users/mbc/mgnify-asa/input/ERZ857107.csv --outdir results/ERZ857107 -with-tower

The MGnify assembly analysis pipeline performs comprehensive annotation of metagenomic assemblies. The pipeline employs the Combined Gene Caller for coding sequence prediction (Richardson et al. 2022), followed by functional annotation using InterProScan for protein annotation (Jones et al. 2014), dbCAN for carbohydrate-active enzyme annotation (Zheng et al. 2023), and HMMER (Eddy 2011) for homology searches against the KEGG database (Aramaki 2019). Taxonomic classification is performed using CAT (von Meijenfeldt et al. 2019), while biosynthetic gene cluster identification is performed antiSMASH (Blin et al. 2021) and SanntiS (Sanchez et al. 2023). Additional tools support various pipeline functions, including Infernal for RNA homology searches (Nawrocki & Eddy 2013), QUAST for assembly quality assessment (Gurevich et al. 2013), and MultiQC for report aggregation (Ewels et al. 2016). Metabolic pathway analysis is performed with DRAM (Shaffer et al. 2020) and Genome Properties (Richardson et al. 2018), with visualization of the taxonomic annotations with with Krona (Ondov et al. 2011) and data processing facilitated by SeqKit (Shen et al. 2016), tabix (Li et al. 2009), FragGeneScanRs (Van der Jeugt et al. 2022), Pyrodigal (Larralde 2022), and Genome Tools (Gremme et al. 2013).

References

• Richardson L, Allen B, Baldi G, Beracochea M, Bileschi ML, Burdett T, et al. MGnify: the microbiome sequence data analysis resource in 2023 [Internet]. Vol. 51, Nucleic Acids Research. Oxford University Press (OUP); 2022. p. D753–9. doi: 10.1093/nar/gkac1080
• Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
• Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
• Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
• da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
• Richardson, L., Allen, B., Baldi, G., Beracochea, M., Bileschi, M. L., Burdett, T., et al. (2022). MGnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Research, 51, D753–D759. doi: 10.1093/nar/gkac1080
• Richardson, L. J., Rawlings, N. D., Salazar, G. A., Almeida, A., Haft, D. R., Ducq, G., et al. (2018). Genome properties in 2019: a new companion database to InterPro for the inference of complete functional attributes. Nucleic Acids Research, 47, D564–D572. doi: 10.1093/nar/gky1013
• Buchfink, B., Reuter, K., & Drost, H. G. (2021). Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods, 18, 366–368. doi: 10.1038/s41592-021-01101-x
• Nawrocki, E. P., & Eddy, S. R. (2013). Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics, 29, 2933–2935. doi: 10.1093/bioinformatics/btt509
• Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011). Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 12, 385. doi: 10.1186/1471-2105-12-385
• Larralde, M. (2022). Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. Journal of Open Source Software, 7, 4296. doi: 10.21105/joss.04296
• Zheng, J., Ge, Q., Yan, Y., Zhang, X., Huang, L., & Yin, Y. (2023). dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Research, 51, W115–W122. doi: 10.1093/nar/gkad328
• Jones, P., Binns, D., Chang, H. Y., Fraser, M., Li, W., McAnulla, C., et al. (2014). InterProScan 5: genome-scale protein function classification. Bioinformatics, 30, 1236–1240. doi: 10.1093/bioinformatics/btu031
• von Meijenfeldt, F. A. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H., & Dutilh, B. E. (2019). Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology, 20, 217. doi: 10.1186/s13059-019-1817-x
• Van der Jeugt, F., Dawyndt, P., & Mesuere, B. (2022). FragGeneScanRs: faster gene prediction for short reads. BMC Bioinformatics, 23, 198. doi: 10.1186/s12859-022-04736-5
• Sanchez, S., Rogers, J. D., Rogers, A. B., Nassar, M., McEntyre, J., Welch, M., et al. (2023). Expansion of novel biosynthetic gene clusters from diverse environments using SanntiS. bioRxiv. doi: 10.1101/2023.05.23.540769
• Shaffer, M., Borton, M. A., McGivern, B. B., Zayed, A. A., La Rosa, S. L., Solden, L. M., et al. (2020). DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Research, 48, 8883–8900. doi: 10.1093/nar/gkaa621
• Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25, 2078–2079. doi: 10.1093/bioinformatics/btp352
• Blin, K., Shaw, S., Kloosterman, A. M., Charlop-Powers, Z., van Wezel, G. P., Medema, M. H., & Weber, T. (2021). antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Research, 49(W1), W29–W35. doi: 10.1093/nar/gkab335
• Eddy, S. R. (2011). Accelerated Profile HMM Searches. PLoS Computational Biology, 7, e1002195. doi: 10.1371/journal.pcbi.1002195
• Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: quality assessment tool for genome assemblies. Bioinformatics, 29, 1072–1075. doi: 10.1093/bioinformatics/btt086
• Shen, W., Le, S., Li, Y., & Hu, F. (2016). SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLOS ONE, 11, e0163962. doi: 10.1371/journal.pone.0163962
• Gremme, G., Steinbiss, S., & Kurtz, S. (2013). GenomeTools: A Comprehensive Software Library for Efficient Processing of Structured Genome Annotations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(3), 645–656. doi: 10.1109/TCBB.2013.68
• Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. doi: /10.1093/bioinformatics/btw354